BUG: Series.replace widens dtype to object when imputing pd.NA

[randolf-scholz]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas
x = pandas.Series([1.0, 3.1, float('nan')])
print(x.dtype)
y = x.replace(float('nan'), pandas.NA)
print(y.dtype)    # object ✘ (expected: Float64)
z = x.replace(1, 1+1j)
print(z.dtype)    # complex128 ✔

Issue Description

Replacing a value with pandas.NA widens the type to object.

Expected Behavior

The data type should only be widened as much as necessary. One would expect overloaded signatures such as

@overload
def replace(self: Series[np.float32], to_replace: np.float32, value: NAType) -> Series[Float32Dtype]: ...
@overload
def replace(self: Series[np.float64], to_replace: np.float64, value: NAType) -> Series[Float64Dtype]: ...
@overload
def replace(self: Series[np.int32], to_replace: np.int32, value: NAType) -> Series[Int32Dtype]: ...
@overload
def replace(self: Series[np.int64], to_replace: np.int64, value: NAType) -> Series[Int64Dtype]: ...
@overload
def replace(self: Series[np.bool_], to_replace: np.bool_, value: NAType) -> Series[BooleanDtype]: ...

related: pandas-dev/pandas-stubs#262

Installed Versions

INSTALLED VERSIONS

commit : ca60aab
python : 3.10.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-47-generic
Version : #51-Ubuntu SMP Thu Aug 11 07:51:15 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.4.4
numpy : 1.23.2
pytz : 2022.2.1
dateutil : 2.8.2
setuptools : 65.3.0
pip : 22.2.2
Cython : None
pytest : 7.1.2
hypothesis : None
sphinx : 5.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.4.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
markupsafe : 2.1.1
matplotlib : 3.5.3
numba : None
numexpr : 2.8.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 9.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.9.1
snappy : None
sqlalchemy : None
tables : 3.7.0
tabulate : 0.8.10
xarray : None
xlrd : None
xlwt : None
zstandard : None

[phofl]

Hi, thanks for your report. Numpy Dtypes can not hold pd.NA, hence we are casting to object. You have to use nullable dtypes explicitly